Distortion minimization in gaussian layered broadcast coding with successive refinement

Chris T.K. Ng, Deniz Gündüz, Andrea J. Goldsmith, Elza Erkip

Research output: Contribution to journalArticle

Abstract

A transmitter without channel state information wishes to send a delay-limited Gaussian source over a slowly fading channel. The source is coded in superimposed layers, with each layer successively refining the description in the previous one. The receiver decodes the layers that are supported by the channel realization and reconstructs the source up to a distortion. The expected distortion is minimized by optimally allocating the transmit power among the source layers. For two source layers, the allocation is optimal when power is first assigned to the higher layer up to a power ceiling that depends only on the channel fading distribution; all remaining power, if any, is allocated to the lower layer. For convex distortion cost functions with convex constraints, the minimization is formulated as a convex optimization problem. In the limit of a continuum of infinite layers, the minimum expected distortion is given by the solution to a set of linear differential equations in terms of the density of the fading distribution. As the number of channel uses per source symbol tends to zero, the power distribution that minimizes expected distortion converges to the one that maximizes expected capacity.

Original languageEnglish (US)
Pages (from-to)5074-5086
Number of pages13
JournalIEEE Transactions on Information Theory
Volume55
Issue number11
DOIs
StatePublished - Nov 10 2009

Keywords

  • Broadcast
  • Convex optimization
  • Distortion
  • Fading channels
  • Power allocation
  • Source-channel coding
  • Successive refinement
  • Superposition

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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